A quantitative analysis of dynamic signal processing by transcription factors

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Project Summary Cells respond to a wide range of environmental cues through intracellular signaling pathways. An increasing number of studies revealed that cells transmit environmental information by controlling the temporal dynamics of activities of signaling molecules. However, understanding how these dynamic patterns are decoded to influence cellular responses remains a challenging goal. Protein kinase A (PKA) is a highly conserved prototypic kinase that regulates many cellular behaviors, such as growth and stress resistance, through transcriptional programs. In response to environmental stimuli, PKA displays various dynamics of signaling activity. Defects in dynamic regulation of PKA activity can lead to disastrous diseases, such as cancer and heart disease. To understand the decoding mechanisms and functions of PKA dynamics, we recently developed a synthetic system in yeast S.cerevisiae that enables direct and precise control of intracellular signaling activities. This system is capable of revealing causal relationships and direct mechanistic connections between signaling dynamics and downstream responses, and hence is a useful tool for developing mechanistic models of signaling systems. In the proposed research, this dynamic control system will be integrated with single-cell imaging, high-throughput microfluidics and computational modeling to produce temporally controlled PKA inputs and to quantitatively investigate how these signaling dynamics are decoded to influence gene expression responses via a single transcription factor (TF) that is dynamically regulated, via two paralogous TFs with distinct dynamics, and via transcriptional network motifs. Modeling analysis suggests that target genes decode dynamics of TF input based on the kinetic properties of their promoters. In Aim 1, the kinetics of molecular processes that govern gene responses to TF dynamics will be determined and this information will be used to develop detailed kinetic models of transcription. These models will be further used and improved in Aim 2 to analyze how two seemingly redundant TFs can distinctly process signaling dynamics and how they contribute to the dynamic diversity of transcriptional responses. Single-cell imaging analysis will be used to test the model results. In Aim 3, we will build on the models from previous aims and investigate how network motifs, composed by multiple TFs and target genes, process and decode signaling dynamics. A high- throughput microfluidic platform will be used to track the abundance and subcellular localization of each motif components in single cells. Based on the single-cell data, modeling analysis will be conducted to analyze how distinct motifs diversify the dynamic responses to differentially decode temporal patterns of signaling inputs. The completion of this project will lead to a quantitative understanding about the decoding mechanisms and functional relevance of signaling dynamics and will lay the scientific foundation for computationally-guided pharmacological treatments of human diseases.